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cs.IR2025
PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
Kepu Zhang, Teng Shi, Weijie Yu +1
Personalized retrieval-augmented generation (RAG) aims to produce user-tailored responses by incorporating retrieved user profiles alongside the input query. Existing methods prima…
cs.IR2025
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors
Kepu Zhang, Zhongxiang Sun, Weijie Yu +5
Retriever-augmented generation (RAG) has become a widely adopted approach for enhancing the factual accuracy of large language models (LLMs). While current benchmarks evaluate the…
cs.IR2024★ 2 cited
Logic Rules as Explanations for Legal Case Retrieval
Zhongxiang Sun, Kepu Zhang, Weijie Yu +2
In this paper, we address the issue of using logic rules to explain the results from legal case retrieval. The task is critical to legal case retrieval because the users (e.g., law…